PROJECT SUMMARY/ABSTRACT Breast cancer is the most frequently diagnosed cancer and the second leading cause of cancer death in American women. Lumpectomy followed by radiotherapy (RT) has significantly improved survival. However, about 42%-47% of patients develop worse early adverse skin reactions (EASRs), pain, breast edema and poor cosmetic results that impact quality of life. Inter-individual variability in the development of RT-induced adverse reactions in normal tissue is well-documented for both acute and late effects. African-American (AA) and underserved populations are less likely than Whites to receive the recommended adjuvant RT, if treated, have a higher risk for developing RT-related side effects and worse clinical outcome. To achieve our long-term goals in improving quality of life, clinical outcome, and overcoming breast cancer disparities, we will use a metabolomics approach to test prediction models for RT-induced EASRs/pain in three racial/ethnic populations. We will test a new paradigm that multiple metabolites/pathways contribute to radiation sensitivity that may predict RT-induced EASRs/pain. Investigating this new paradigm will develop powerful tools in identifying high- risk populations and targets for precision intervention and treatment. Aim 1 will evaluate global metabolomics - driven prediction models of RT-induced EASRs/pain. Aim 2 will quantitate and validate significant metabolites and pathways from Aim 1 in predicting RT-induced EASRs/pain. Aim 3 will develop and assess the performance of comprehensive model (i.e., metabolomics/pathways from Aim 2, patient characteristics, and clinical variables) in predicting RT-induced EASRs/pain. Our preliminary data showed that the alanine, aspartate and glutamate metabolism pathway may contribute to RT-induced EASRs/pain. Thus, targeting tumor metabolism by glutaminase inhibitors may prevent RT-induced EASRs/pain in addition to their anti-tumor activities. Capitalizing on existing urine samples and clinical data from a large tri-racial/ethnic breast cancer population to target 480 patients (67% minorities), promising pilot data, state-of-the-art metabolomics technologies, innovative bioinformatics approach, and skilled multidisciplinary team, we are in an exceptional position to carry out the proposed research. To the best of our knowledge, this is the first metabolomics study of RT-induced EASRs/pain in breast cancer. Using a hypothesis-driven approach, the outcomes will significantly improve the accuracy of urgently needed prediction models of RT-induced EASRs/pain in breast cancer. The outcomes of the proposed research will advance our scientific knowledge in the accurate assessment of RT outcomes and targeted intervention. They also may enable the development of benefit-risk indices that will aid in the decision and planning of RT. The outcome will target effective intervention and treatment strategies, and ultimately improve the quality of life and progression-free survival in breast cancer patients, particularly in underserved minorities with worse RT-induced EASRs/pain and clinical outcome.